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Record W4402401412 · doi:10.1016/j.lcsi.2024.100852

Teacher collaborative inquiry into practice in school-based learning communities: The role of activity type

2024· article· en· W4402401412 on OpenAlexfundno aff
Miriam Babichenko, Adam Lefstein, Christa S. C. Asterhan

Bibliographic record

VenueLearning Culture and Social Interaction · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersYad HanadivAzrieli Foundation
KeywordsPedagogyPsychologyCollaborative learningMathematics education

Abstract

fetched live from OpenAlex

This study contributes to growing scholarly interest in teacher-led, school-based learning communities and the characteristics of teacher dialogue and social interaction that support professional learning in these settings. Based on existing conceptual distinctions proposed in the literature, we term this type of teacher dialogue “collaborative inquiry into practice” (CLIP) and propose a systematic and reliable tool to measure it. We then employ a quantitative, comparative research design to study how different teacher team activities (i.e., video-analysis, peer consultations, and pedagogical planning) shape the extent to which teachers engage in CLIP. Fifty-four transcribed teacher meeting excerpts were analyzed with the CLIP coding scheme, assessing different aspects of inquiry-based reasoning, participation, and content. Quantitative comparisons and illustrative examples show that CLIP was lowest during peer consultations, in part because teachers were often not positioned as agents of change in such conversations. Pedagogical planning activities featured more instances of inquiry into each other's ideas. Contrary to common assumptions, collaborative video analysis activities were not characterized by increased attention to student thinking or inquiry orientation. Our findings provide new insights into teacher-led, collaborative learning in on-the-job settings, as well as practical implications for the design of school-based professional learning communities .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.073
GPT teacher head0.439
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2024
Admission routes1
Has abstractyes

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